IT decision makers should evaluate both prompt caching and Retrieval-Augmented Generation (RAG) as complementary tools in their LLM strategy. Prompt caching brings speed and savings, while RAG delivers context-rich accuracy. Plan for both, instead of choosing between them.
Prompt caching is a must-have for IT leaders aiming to optimize AI application performance. Implementing prompt caching can lead to up to cost savings and faster response times, especially in applications with repetitive or large-context prompts.
CIOs should prioritize thorough security audits for local large language models (LLMs) to mitigate risks while ensuring compliance and safeguarding sensitive data. While deploying LLMs locally enhances data sovereignty, neglecting security protocols can lead to vulnerabilities.
Not every “open” AI model is truly open. Many are just open-weight, sharing pre-trained weights while keeping the code, data, and broader usage rights under wraps. Confusing the two can lead to compliance pitfalls, limited flexibility, and missed chances to innovate. CIOs should create a governance framework that draws a clear line between open-weight and open-source models to ensure AI investments are both strategically sound and legally safe.
The International Olympic Committee’s AI Agenda shows that AI isn’t just a competitive edge for teams anymore—it’s a playbook for operations, safety, and fan engagement. CIOs of major sporting bodies should take the field early by piloting AI solutions in content moderation and operations management.
AI may be the future, but don’t let it feed off your content buffet without a reservation. CIOs should immediately audit and block unauthorized AI web crawlers to regain control over their digital IP. This isn’t about stalling innovation, it’s about steering it in your favor.
